Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells4728
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 4 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 1 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 646 (1.8%) missing valuesMissing
PM10 has 381 (1.1%) missing valuesMissing
SO2 has 507 (1.4%) missing valuesMissing
NO2 has 668 (1.9%) missing valuesMissing
CO has 1401 (4.0%) missing valuesMissing
O3 has 729 (2.1%) missing valuesMissing
RAIN is highly skewed (γ1 = 27.26885148)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33673 (96.0%) zerosZeros
WSPM has 2492 (7.1%) zerosZeros

Reproduction

Analysis started2024-03-08 05:11:21.538090
Analysis finished2024-03-08 05:12:04.198697
Duration42.66 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:04.351815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:12:04.609394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:12:04.840023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:12:05.029162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:05.311166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:12:05.440420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:05.643359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:12:05.929458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:06.178489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:12:06.580251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct577
Distinct (%)1.7%
Missing646
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean83.852089
Minimum2
Maximum770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:06.782560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q124
median60
Q3115.75
95-th percentile247
Maximum770
Range768
Interquartile range (IQR)91.75

Descriptive statistics

Standard deviation82.796445
Coefficient of variation (CV)0.98741064
Kurtosis6.6049022
Mean83.852089
Median Absolute Deviation (MAD)41
Skewness2.0922507
Sum2886021.2
Variance6855.2512
MonotonicityNot monotonic
2024-03-08T12:12:07.089391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 563
 
1.6%
11 544
 
1.6%
9 534
 
1.5%
10 526
 
1.5%
7 505
 
1.4%
12 501
 
1.4%
6 454
 
1.3%
3 448
 
1.3%
13 428
 
1.2%
14 395
 
1.1%
Other values (567) 29520
84.2%
(Missing) 646
 
1.8%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 448
1.3%
4 242
0.7%
5 314
0.9%
6 454
1.3%
7 505
1.4%
8 563
1.6%
8.5 1
 
< 0.1%
9 534
1.5%
10 526
1.5%
ValueCountFrequency (%)
770 1
< 0.1%
767 1
< 0.1%
741 1
< 0.1%
739 1
< 0.1%
733 1
< 0.1%
705 2
< 0.1%
680 1
< 0.1%
677 1
< 0.1%
675 1
< 0.1%
661 2
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct660
Distinct (%)1.9%
Missing381
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean118.86198
Minimum2
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:07.365193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q145
median99
Q3167
95-th percentile295
Maximum994
Range992
Interquartile range (IQR)122

Descriptive statistics

Standard deviation96.742626
Coefficient of variation (CV)0.81390725
Kurtosis5.5891621
Mean118.86198
Median Absolute Deviation (MAD)59
Skewness1.7108188
Sum4122490
Variance9359.1356
MonotonicityNot monotonic
2024-03-08T12:12:07.623493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 412
 
1.2%
19 246
 
0.7%
11 246
 
0.7%
17 245
 
0.7%
13 244
 
0.7%
18 239
 
0.7%
20 239
 
0.7%
21 238
 
0.7%
5 237
 
0.7%
12 235
 
0.7%
Other values (650) 32102
91.6%
(Missing) 381
 
1.1%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 59
 
0.2%
4 16
 
< 0.1%
5 237
0.7%
6 412
1.2%
7 178
0.5%
8 216
0.6%
9 218
0.6%
10 216
0.6%
11 246
0.7%
ValueCountFrequency (%)
994 1
< 0.1%
986 1
< 0.1%
983 1
< 0.1%
957 1
< 0.1%
947 1
< 0.1%
941 1
< 0.1%
890 1
< 0.1%
883 1
< 0.1%
862 1
< 0.1%
849 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)0.7%
Missing507
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean15.366162
Minimum0.2856
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:07.914251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median7
Q320
95-th percentile57
Maximum500
Range499.7144
Interquartile range (IQR)18

Descriptive statistics

Standard deviation21.204526
Coefficient of variation (CV)1.3799494
Kurtosis34.320557
Mean15.366162
Median Absolute Deviation (MAD)5
Skewness3.6894519
Sum531008.45
Variance449.63192
MonotonicityNot monotonic
2024-03-08T12:12:08.236045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8948
25.5%
3 3390
 
9.7%
4 1852
 
5.3%
5 1448
 
4.1%
6 1262
 
3.6%
7 1111
 
3.2%
8 1041
 
3.0%
9 798
 
2.3%
10 767
 
2.2%
11 739
 
2.1%
Other values (233) 13201
37.6%
ValueCountFrequency (%)
0.2856 1
 
< 0.1%
0.8568 1
 
< 0.1%
1 195
 
0.6%
1.1424 1
 
< 0.1%
1.9992 1
 
< 0.1%
2 8948
25.5%
2.2848 1
 
< 0.1%
2.5704 1
 
< 0.1%
2.856 1
 
< 0.1%
3 3390
 
9.7%
ValueCountFrequency (%)
500 3
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
227 1
 
< 0.1%
220 2
< 0.1%
218 1
 
< 0.1%
202 1
 
< 0.1%
192 2
< 0.1%
188 1
 
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct367
Distinct (%)1.1%
Missing668
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean55.871075
Minimum2
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:08.467464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q126
median50
Q379
95-th percentile122
Maximum276
Range274
Interquartile range (IQR)53

Descriptive statistics

Standard deviation36.47386
Coefficient of variation (CV)0.65282188
Kurtosis0.79068693
Mean55.871075
Median Absolute Deviation (MAD)26
Skewness0.87862412
Sum1921741.5
Variance1330.3425
MonotonicityNot monotonic
2024-03-08T12:12:08.681750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 473
 
1.3%
16 462
 
1.3%
11 453
 
1.3%
13 444
 
1.3%
18 441
 
1.3%
10 439
 
1.3%
14 426
 
1.2%
17 425
 
1.2%
25 419
 
1.2%
22 416
 
1.2%
Other values (357) 29998
85.6%
(Missing) 668
 
1.9%
ValueCountFrequency (%)
2 71
 
0.2%
3 68
 
0.2%
4 116
0.3%
5 154
0.4%
5.7484 1
 
< 0.1%
5.9537 1
 
< 0.1%
6 202
0.6%
6.3643 1
 
< 0.1%
7 252
0.7%
7.5961 2
 
< 0.1%
ValueCountFrequency (%)
276 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
261 1
< 0.1%
258 1
< 0.1%
240 1
< 0.1%
239 1
< 0.1%
237 1
< 0.1%
229 1
< 0.1%
226 2
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)0.3%
Missing1401
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1323.9744
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:08.964525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1600
median900
Q31600
95-th percentile3700
Maximum10000
Range9900
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation1208.9578
Coefficient of variation (CV)0.91312774
Kurtosis8.5720053
Mean1323.9744
Median Absolute Deviation (MAD)400
Skewness2.4567288
Sum44568951
Variance1461578.9
MonotonicityNot monotonic
2024-03-08T12:12:09.275229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 2632
 
7.5%
600 2377
 
6.8%
400 2328
 
6.6%
700 2321
 
6.6%
500 2272
 
6.5%
800 2072
 
5.9%
900 1961
 
5.6%
1000 1738
 
5.0%
1100 1630
 
4.6%
1200 1337
 
3.8%
Other values (106) 12995
37.1%
(Missing) 1401
 
4.0%
ValueCountFrequency (%)
100 221
 
0.6%
200 846
 
2.4%
300 2632
7.5%
400 2328
6.6%
500 2272
6.5%
600 2377
6.8%
700 2321
6.6%
800 2072
5.9%
900 1961
5.6%
1000 1738
5.0%
ValueCountFrequency (%)
10000 3
 
< 0.1%
9900 10
< 0.1%
9800 9
< 0.1%
9700 9
< 0.1%
9600 6
< 0.1%
9500 1
 
< 0.1%
9400 2
 
< 0.1%
9300 5
< 0.1%
9200 4
 
< 0.1%
9000 8
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct804
Distinct (%)2.3%
Missing729
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean57.694879
Minimum0.2142
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:09.525588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q110
median45
Q383
95-th percentile180
Maximum450
Range449.7858
Interquartile range (IQR)73

Descriptive statistics

Standard deviation57.019587
Coefficient of variation (CV)0.98829546
Kurtosis2.1676576
Mean57.694879
Median Absolute Deviation (MAD)36
Skewness1.4341318
Sum1980953.7
Variance3251.2333
MonotonicityNot monotonic
2024-03-08T12:12:09.740976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2476
 
7.1%
3 975
 
2.8%
4 922
 
2.6%
5 841
 
2.4%
6 713
 
2.0%
7 624
 
1.8%
8 552
 
1.6%
9 497
 
1.4%
10 445
 
1.3%
11 375
 
1.1%
Other values (794) 25915
73.9%
(Missing) 729
 
2.1%
ValueCountFrequency (%)
0.2142 1
 
< 0.1%
0.4284 2
 
< 0.1%
0.6426 1
 
< 0.1%
0.8568 6
 
< 0.1%
1 179
0.5%
1.071 10
 
< 0.1%
1.2852 10
 
< 0.1%
1.4994 16
 
< 0.1%
1.7136 7
 
< 0.1%
1.9278 14
 
< 0.1%
ValueCountFrequency (%)
450 1
 
< 0.1%
374 1
 
< 0.1%
359 1
 
< 0.1%
357 1
 
< 0.1%
355 1
 
< 0.1%
349 1
 
< 0.1%
348 3
< 0.1%
346 1
 
< 0.1%
345 1
 
< 0.1%
342 1
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct995
Distinct (%)2.8%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.864524
Minimum-15.6
Maximum41.6
Zeros191
Zeros (%)0.5%
Negative4887
Negative (%)13.9%
Memory size274.1 KiB
2024-03-08T12:12:10.040014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-15.6
5-th percentile-3.8
Q13.6
median14.8
Q323.5
95-th percentile30.8
Maximum41.6
Range57.2
Interquartile range (IQR)19.9

Descriptive statistics

Standard deviation11.292857
Coefficient of variation (CV)0.81451456
Kurtosis-1.1432736
Mean13.864524
Median Absolute Deviation (MAD)9.7
Skewness-0.10168203
Sum485438.6
Variance127.52862
MonotonicityNot monotonic
2024-03-08T12:12:10.339808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 283
 
0.8%
2 226
 
0.6%
1 197
 
0.6%
0 191
 
0.5%
-1 170
 
0.5%
-2 167
 
0.5%
4 157
 
0.4%
5 154
 
0.4%
23.8 143
 
0.4%
22.4 137
 
0.4%
Other values (985) 33188
94.6%
ValueCountFrequency (%)
-15.6 1
 
< 0.1%
-15.5 1
 
< 0.1%
-15.2 2
< 0.1%
-15.1 3
< 0.1%
-15 3
< 0.1%
-14.9 1
 
< 0.1%
-14.8 3
< 0.1%
-14.7 2
< 0.1%
-14.6 1
 
< 0.1%
-14.3 1
 
< 0.1%
ValueCountFrequency (%)
41.6 1
< 0.1%
40.9 1
< 0.1%
40.2 1
< 0.1%
39.8 1
< 0.1%
39.3 1
< 0.1%
39 2
< 0.1%
38.8 1
< 0.1%
38.7 1
< 0.1%
38.1 1
< 0.1%
37.9 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct592
Distinct (%)1.7%
Missing50
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1008.8296
Minimum984
Maximum1038.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:10.611264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum984
5-th percentile993.6
Q11000.5
median1008.5
Q31017
95-th percentile1025.1
Maximum1038.1
Range54.1
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation10.103256
Coefficient of variation (CV)0.010014829
Kurtosis-0.90214345
Mean1008.8296
Median Absolute Deviation (MAD)8.3
Skewness0.099001783
Sum35323159
Variance102.07578
MonotonicityNot monotonic
2024-03-08T12:12:10.878361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019 285
 
0.8%
1020 280
 
0.8%
1016 270
 
0.8%
1018 262
 
0.7%
1021 253
 
0.7%
1015 246
 
0.7%
1022 240
 
0.7%
1017 223
 
0.6%
1012 215
 
0.6%
1010 213
 
0.6%
Other values (582) 32527
92.8%
ValueCountFrequency (%)
984 3
< 0.1%
984.2 2
< 0.1%
984.3 1
 
< 0.1%
984.4 2
< 0.1%
984.6 1
 
< 0.1%
984.8 2
< 0.1%
985.1 1
 
< 0.1%
985.2 1
 
< 0.1%
985.3 1
 
< 0.1%
985.4 1
 
< 0.1%
ValueCountFrequency (%)
1038.1 1
 
< 0.1%
1038 1
 
< 0.1%
1037.5 2
< 0.1%
1037.3 4
< 0.1%
1037.2 1
 
< 0.1%
1037.1 2
< 0.1%
1037 1
 
< 0.1%
1036.9 3
< 0.1%
1036.6 3
< 0.1%
1036.5 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct594
Distinct (%)1.7%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.6104418
Minimum-34.6
Maximum27.4
Zeros56
Zeros (%)0.2%
Negative15362
Negative (%)43.8%
Memory size274.1 KiB
2024-03-08T12:12:11.124215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-34.6
5-th percentile-19.6
Q1-8.9
median3
Q315.3
95-th percentile22.3
Maximum27.4
Range62
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation13.782991
Coefficient of variation (CV)5.2799455
Kurtosis-1.15798
Mean2.6104418
Median Absolute Deviation (MAD)12.1
Skewness-0.16804275
Sum91399.4
Variance189.97083
MonotonicityNot monotonic
2024-03-08T12:12:11.330031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.2 139
 
0.4%
17.4 138
 
0.4%
17.3 137
 
0.4%
16.8 133
 
0.4%
16.4 128
 
0.4%
17.7 126
 
0.4%
16.7 122
 
0.3%
17.5 121
 
0.3%
16.3 119
 
0.3%
18.7 118
 
0.3%
Other values (584) 33732
96.2%
ValueCountFrequency (%)
-34.6 1
< 0.1%
-34.4 1
< 0.1%
-34.3 2
< 0.1%
-34.1 1
< 0.1%
-33.7 1
< 0.1%
-33.6 1
< 0.1%
-33.5 1
< 0.1%
-33.4 1
< 0.1%
-32.9 1
< 0.1%
-32.8 1
< 0.1%
ValueCountFrequency (%)
27.4 1
 
< 0.1%
27.2 2
 
< 0.1%
27.1 1
 
< 0.1%
27 1
 
< 0.1%
26.9 1
 
< 0.1%
26.8 5
< 0.1%
26.7 2
 
< 0.1%
26.6 4
< 0.1%
26.5 5
< 0.1%
26.4 5
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct125
Distinct (%)0.4%
Missing43
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.064452757
Minimum0
Maximum41.9
Zeros33673
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:12.122006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum41.9
Range41.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83865386
Coefficient of variation (CV)13.011916
Kurtosis947.38339
Mean0.064452757
Median Absolute Deviation (MAD)0
Skewness27.268851
Sum2257.2
Variance0.7033403
MonotonicityNot monotonic
2024-03-08T12:12:12.406513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33673
96.0%
0.1 281
 
0.8%
0.2 151
 
0.4%
0.3 132
 
0.4%
0.6 77
 
0.2%
0.4 74
 
0.2%
0.5 62
 
0.2%
0.8 46
 
0.1%
0.7 45
 
0.1%
0.9 37
 
0.1%
Other values (115) 443
 
1.3%
(Missing) 43
 
0.1%
ValueCountFrequency (%)
0 33673
96.0%
0.1 281
 
0.8%
0.2 151
 
0.4%
0.3 132
 
0.4%
0.4 74
 
0.2%
0.5 62
 
0.2%
0.6 77
 
0.2%
0.7 45
 
0.1%
0.8 46
 
0.1%
0.9 37
 
0.1%
ValueCountFrequency (%)
41.9 1
< 0.1%
39 1
< 0.1%
36.6 1
< 0.1%
34.5 1
< 0.1%
33.5 1
< 0.1%
30.5 1
< 0.1%
30.4 1
< 0.1%
30.3 1
< 0.1%
29.2 1
< 0.1%
24.1 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing159
Missing (%)0.5%
Memory size274.1 KiB
N
3949 
NE
3119 
SSW
2762 
NW
2758 
NNE
2602 
Other values (11)
19715 

Length

Max length3
Median length2
Mean length2.1499212
Min length1

Characters and Unicode

Total characters75043
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowNW
3rd rowWNW
4th rowW
5th rowWNW

Common Values

ValueCountFrequency (%)
N 3949
11.3%
NE 3119
 
8.9%
SSW 2762
 
7.9%
NW 2758
 
7.9%
NNE 2602
 
7.4%
S 2343
 
6.7%
W 2331
 
6.6%
WNW 2073
 
5.9%
SW 1993
 
5.7%
ENE 1885
 
5.4%
Other values (6) 9090
25.9%

Length

2024-03-08T12:12:12.722963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 3949
11.3%
ne 3119
 
8.9%
ssw 2762
 
7.9%
nw 2758
 
7.9%
nne 2602
 
7.5%
s 2343
 
6.7%
w 2331
 
6.7%
wnw 2073
 
5.9%
sw 1993
 
5.7%
ene 1885
 
5.4%
Other values (6) 9090
26.0%

Most occurring characters

ValueCountFrequency (%)
N 22216
29.6%
W 18846
25.1%
S 17282
23.0%
E 16699
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 75043
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 22216
29.6%
W 18846
25.1%
S 17282
23.0%
E 16699
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 75043
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 22216
29.6%
W 18846
25.1%
S 17282
23.0%
E 16699
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 22216
29.6%
W 18846
25.1%
S 17282
23.0%
E 16699
22.3%

WSPM
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.3%
Missing42
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.3433099
Minimum0
Maximum12
Zeros2492
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:12:12.954260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median1
Q31.8
95-th percentile3.6
Maximum12
Range12
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1510644
Coefficient of variation (CV)0.85688669
Kurtosis5.8353117
Mean1.3433099
Median Absolute Deviation (MAD)0.5
Skewness1.9025955
Sum47045.4
Variance1.3249492
MonotonicityNot monotonic
2024-03-08T12:12:13.196391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2492
 
7.1%
0.7 2176
 
6.2%
0.6 2065
 
5.9%
0.8 2033
 
5.8%
0.9 1944
 
5.5%
0.5 1868
 
5.3%
1 1740
 
5.0%
1.1 1507
 
4.3%
1.2 1434
 
4.1%
0.4 1348
 
3.8%
Other values (91) 16415
46.8%
ValueCountFrequency (%)
0 2492
7.1%
0.1 931
 
2.7%
0.2 860
 
2.5%
0.3 176
 
0.5%
0.4 1348
3.8%
0.5 1868
5.3%
0.6 2065
5.9%
0.7 2176
6.2%
0.8 2033
5.8%
0.9 1944
5.5%
ValueCountFrequency (%)
12 1
 
< 0.1%
11.4 1
 
< 0.1%
11 1
 
< 0.1%
10.9 1
 
< 0.1%
10.7 1
 
< 0.1%
10.1 1
 
< 0.1%
9.8 2
< 0.1%
9.6 1
 
< 0.1%
9.5 1
 
< 0.1%
9.1 3
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Gucheng
35064 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters245448
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGucheng
2nd rowGucheng
3rd rowGucheng
4th rowGucheng
5th rowGucheng

Common Values

ValueCountFrequency (%)
Gucheng 35064
100.0%

Length

2024-03-08T12:12:13.459973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:12:13.625538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
gucheng 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
G 35064
14.3%
u 35064
14.3%
c 35064
14.3%
h 35064
14.3%
e 35064
14.3%
n 35064
14.3%
g 35064
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210384
85.7%
Uppercase Letter 35064
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 35064
16.7%
c 35064
16.7%
h 35064
16.7%
e 35064
16.7%
n 35064
16.7%
g 35064
16.7%
Uppercase Letter
ValueCountFrequency (%)
G 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 245448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 35064
14.3%
u 35064
14.3%
c 35064
14.3%
h 35064
14.3%
e 35064
14.3%
n 35064
14.3%
g 35064
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 35064
14.3%
u 35064
14.3%
c 35064
14.3%
h 35064
14.3%
e 35064
14.3%
n 35064
14.3%
g 35064
14.3%

Interactions

2024-03-08T12:11:59.808700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:23.660993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.041297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.052880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.597142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:34.204087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.867715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:39.555955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.344654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.451269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.536067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:49.161046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.495695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.877061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:57.091610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.991880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:23.800189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.252872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.286478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.765736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:34.373170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.011505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:39.765031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.517142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.621883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.681900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:49.359204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.728445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:54.061348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:57.287570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:00.116686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:23.944451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.470948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.453839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.933519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:34.589653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.149954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:39.912252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.655425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.759505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.914666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:49.530412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.892894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:54.226578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:57.775954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:00.274848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.095395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.611498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.621686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:32.116157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:34.753965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.321232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:40.141806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.811424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.899224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.103066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:49.687829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.046278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:54.383646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:57.959441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:00.486365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.223614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.750344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.838679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:32.284208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:34.948581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.525186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:40.274899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.936960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.053119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.266428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:49.842791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.214829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:54.590450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.129854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:00.753239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.485005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:26.921416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:29.987327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:32.512949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:35.171768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.729892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:40.530867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.082155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.200410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.408984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.046128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.371467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:54.768556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.310063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:00.953981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.645296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:27.043451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.132010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:32.711474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:35.316046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.864393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:40.731225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.218374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.333050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.520040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.207731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.506878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:55.009414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.482946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:01.204561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.783212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:27.191847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.316940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:32.911905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:35.450235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:37.996930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:40.929154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.354945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.470957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.863482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.342098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.629594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:55.262347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.619138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:01.443997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:24.913358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:27.334125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.474066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.094469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:35.674563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:38.127876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.143511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.483097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.621144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:47.986952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.493333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.766936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:55.503017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.753743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:01.614666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.091698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:27.477044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.619481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.233971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:35.936080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:38.519477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.339273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.611315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.740855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.119302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.631381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:52.903308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:55.748013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:58.973026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:01.756346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.230704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:27.756913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.797902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.354156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.113007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:38.659054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.483292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.751556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:45.869666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.234733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.767475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.031608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:55.981733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.102811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:01.963475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.356487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:28.033211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:30.961301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.510150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.284206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:38.794309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.645767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:43.888069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.010740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.396827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:50.904439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.179152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:56.226819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.237585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:02.167533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.494054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:28.262898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.094946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.670296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.433107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:38.927690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.840902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.013324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.151272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.603086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.053286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.321780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:56.479527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.378158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:02.348196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.669581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:28.448118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.284579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.847718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.571859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:39.145941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:41.976092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.157273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.292108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.821332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.225786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.488767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:56.763784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.527506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:02.539352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:25.844181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:28.575764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:31.424729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:33.996143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:36.700050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:39.337957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:42.186596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:44.298141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:46.409001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:48.993610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:51.357036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:53.676260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:56.917499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:59.671014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:12:13.760542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.000-0.0110.780-0.064-0.5160.6700.8050.182-0.0210.577-0.319-0.3540.000-0.0150.0270.0570.084
DEWP-0.0111.0000.036-0.1080.2650.1640.217-0.7790.171-0.4880.818-0.3350.023-0.0090.2520.0810.160
NO20.7800.0361.000-0.111-0.6470.6930.7160.100-0.0800.507-0.243-0.4770.019-0.0310.0630.0830.098
No-0.064-0.108-0.1111.000-0.072-0.059-0.0530.1640.007-0.209-0.1290.2470.0180.0010.0440.1520.862
O3-0.5160.265-0.647-0.0721.000-0.247-0.296-0.4480.006-0.2730.6020.411-0.0130.290-0.1760.1310.063
PM100.6700.1640.693-0.059-0.2471.0000.870-0.117-0.1030.4290.006-0.2470.0340.106-0.0290.0550.070
PM2.50.8050.2170.716-0.053-0.2960.8701.000-0.066-0.0340.451-0.047-0.3060.0190.0500.0030.0530.061
PRES0.182-0.7790.1000.164-0.448-0.117-0.0661.000-0.0710.381-0.8360.1080.005-0.037-0.0060.0590.144
RAIN-0.0210.171-0.0800.0070.006-0.103-0.034-0.0711.000-0.1700.030-0.051-0.008-0.0070.0520.0140.009
SO20.577-0.4880.507-0.209-0.2730.4290.4510.381-0.1701.000-0.490-0.0110.0140.041-0.2440.0480.076
TEMP-0.3190.818-0.243-0.1290.6020.006-0.047-0.8360.030-0.4901.0000.0120.0170.1430.1230.0990.153
WSPM-0.354-0.335-0.4770.2470.411-0.247-0.3060.108-0.051-0.0110.0121.0000.0050.191-0.2150.1270.172
day0.0000.0230.0190.018-0.0130.0340.0190.005-0.0080.0140.0170.0051.0000.0000.0100.0300.000
hour-0.015-0.009-0.0310.0010.2900.1060.050-0.037-0.0070.0410.1430.1910.0001.0000.0000.1510.000
month0.0270.2520.0630.044-0.176-0.0290.003-0.0060.052-0.2440.123-0.2150.0100.0001.0000.0740.249
wd0.0570.0810.0830.1520.1310.0550.0530.0590.0140.0480.0990.1270.0300.1510.0741.0000.204
year0.0840.1600.0980.8620.0630.0700.0610.1440.0090.0760.1530.1720.0000.0000.2490.2041.000

Missing values

2024-03-08T12:12:02.911972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:12:03.460039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:12:03.915333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133106.018.05.0NaN800.088.00.11021.1-18.60.0NW4.4Gucheng
1220133116.015.05.0NaN800.088.0-0.31021.5-19.00.0NW4.0Gucheng
2320133125.018.0NaNNaN700.052.0-0.71021.5-19.80.0WNW4.6Gucheng
3420133136.020.06.0NaNNaNNaN-1.01022.7-21.20.0W2.8Gucheng
4520133145.017.05.0NaN600.073.0-1.31023.0-21.40.0WNW3.6Gucheng
5620133154.011.03.0NaN700.087.0-1.81023.6-21.90.0E1.2Gucheng
6720133163.06.03.0NaN700.092.0-2.61024.3-20.40.0ENE1.2Gucheng
7820133175.05.03.0NaN800.086.0-0.91025.6-20.50.0ENE1.1Gucheng
8920133185.09.05.0NaN900.081.00.11026.1-20.30.0ENE3.0Gucheng
91020133194.010.06.0NaN900.082.01.11026.1-20.60.0NE2.8Gucheng
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228143.016.02.02.0300.067.014.81009.5-17.90.0WNW4.3Gucheng
35055350562017228156.029.02.02.0300.073.014.81009.1-17.90.0NNW6.4Gucheng
350563505720172281615.034.03.02.0400.073.015.01008.8-17.70.0NNW5.3Gucheng
350573505820172281712.047.03.04.0400.069.014.11009.0-17.10.0NW4.1Gucheng
350583505920172281812.041.03.010.0400.064.013.41009.4-16.50.0NNW3.8Gucheng
350593506020172281914.058.04.019.0500.056.012.81009.9-17.00.0NNW3.1Gucheng
350603506120172282027.083.06.060.0700.026.011.11010.4-15.50.0NW1.9Gucheng
350613506220172282122.037.07.052.0600.027.010.51010.8-15.90.0N2.3Gucheng
35062350632017228229.023.03.013.0400.057.08.91010.9-14.90.0NE1.6Gucheng
350633506420172282312.048.05.048.0600.028.06.21010.5-13.40.0NNE0.7Gucheng